Machine reading comprehension (MRC), which requires a machine to answer questions
based on a given context, has attracted increasing attention with the incorporation of various …

In the field of artificial intelligence, human–computer interaction (HCI) technology and its
related intelligent robot technologies are essential and interesting contents of research …

The task of named entity recognition (NER) is normally divided into nested NER and flat
NER depending on whether named entities are nested or not. Models are usually separately …

Meetings are a key component of human collaboration. As increasing numbers of meetings
are recorded and transcribed, meeting summaries have become essential to remind those …

State-of-the-art models often make use of superficial patterns in the data that do not
generalize well to out-of-domain or adversarial settings. For example, textual entailment …

With the development of high computational devices, deep neural networks (DNNs), in
recent years, have gained significant popularity in many Artificial Intelligence (AI) …

Standard accuracy metrics indicate that reading comprehension systems are making rapid
progress, but the extent to which these systems truly understand language remains unclear …

Current end-to-end machine reading and question answering (Q\&A) models are primarily
based on recurrent neural networks (RNNs) with attention. Despite their success, these …

Deep learning has improved performance on many natural language processing (NLP)
tasks individually. However, general NLP models cannot emerge within a paradigm that …

In this paper, we propose a new paradigm for the task of entity-relation extraction. We cast
the task as a multi-turn question answering problem, ie, the extraction of entities and …